Native advertising effectiveness depends critically on both the intrinsic quality of the ad creative and its contextual fit with the surrounding article. However, automated quality assessment remains challenging, relying primarily on post-hoc engagement metrics or expensive manual review. This project develops a machine learning model that predicts ad memorability scores (1-10 scale) by combining multimodal creative analysis with contextual alignment features.
We trained an XGBoost regression model on 29,032 ad-article pairs scored by GPT-5.2 vision, achieving strong performance (MAE=0.605, R²=0.785). The model extracts 21 interpretable features spanning visual attention, copy quality, and contextual fit - enabling real-time quality prediction for ad placement optimization.
The key innovation is the decomposition of memorability into intrinsic ad properties (M features) and contextual fit metrics (F features), allowing the mode